GPT-5.4 Mini vs Muse Ember
Muse Ember leads GPT-5.4 Mini on coding, 64.9 to 35.8 on the Vibe Coding Index, and Muse Ember costs 9.0x less per output token ($0.50 vs $4.50 per million). Updated November 14, 2026; 8 benchmarks have a result for both.
GPT-5.4 Mini or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 35.8 for GPT-5.4 Mini, though on 3 benchmarks against 5.
GPT-5.4 Mini is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 8
- GPT-5.4 Mini only
- 85
- Muse Ember only
- 7
- Like-for-like categories
- 2 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
Muse Ember
Muse Ember scores 64.9 against 35.8.
5 and 3 benchmarks behind the scores
Agentic work
AA Agentic Index
No call
Only GPT-5.4 Mini has a AA Agentic Index result, 17.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.4 Mini has a GPQA Diamond result, 83.1%.
Directional only
Math
AIME
No call
Only GPT-5.4 Mini has a AIME result, 95.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.4 Mini has a MMLU Pro result, 84.5%.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
GPT-5.4 Mini $0.0030, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.4 Mini has the number, $0.70.
Directional only
At a glance
- Coding index
- GPT-5.4 Mini 35.8 from 5 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 36.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): GPT-5.4 Mini 2.5% against Muse Ember 39.4%, 49.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.4 Mini $4.50 against Muse Ember $0.50 per million output tokens, 9.0x apart.
- Context window
- GPT-5.4 Mini takes 400K tokens, Muse Ember 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.4 Mini | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Agentic work | 0 | 2 | ||
| All shared boards | 0 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1398 | 1609 | Muse Ember |
| AA Coding Index | 56.1 | Not measured | Only GPT-5.4 Mini measured |
| LiveCodeBench (Vals AI) | 81.5% | Not measured | Only GPT-5.4 Mini measured |
| SWE-bench Verified (Vals AI) | 73.0% | Not measured | Only GPT-5.4 Mini measured |
| Terminal-Bench 4 (Vals AI) | 2.5% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 48.0% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 12.9% | Not measured | Only GPT-5.4 Mini measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GPT-5.4 Mini measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only GPT-5.4 Mini measured |
| SciCode | 52.1% | Not measured | Only GPT-5.4 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 52.3% | Not measured | Only GPT-5.4 Mini measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 2.0% | 40.9% | Muse Ember |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| FrontierCode | 27.0% | Not measured | Only GPT-5.4 Mini measured |
| WeirdML | 60.3% | Not measured | Only GPT-5.4 Mini measured |
| ALE-Bench | 1188.6 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: coding | 1439 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: software and IT | 1437 | Not measured | Only GPT-5.4 Mini measured |
| LMArena WebDev: React | 1386 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1420 | 1621 | Muse Ember |
| GameBench 2 | 46.3 | Not measured | Only GPT-5.4 Mini measured |
| Terminal-Bench 4.0 | 1.8% | Not measured | Only GPT-5.4 Mini measured |
| ProgramBench | 0.0% | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 149.3 | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 17.9 | Not measured | Only GPT-5.4 Mini measured |
| Finance Agent (Vals AI) | 45.4% | Not measured | Only GPT-5.4 Mini measured |
| Tax Agent Bench (Vals AI) | 8.8% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 12.5% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| Excel Modeling Benchmark (Vals AI) | 45.4% | Not measured | Only GPT-5.4 Mini measured |
| τ²-Bench Telecom | 83.3% | Not measured | Only GPT-5.4 Mini measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| DeepResearch Bench | 36.3% | Not measured | Only GPT-5.4 Mini measured |
| MCP Atlas | 56.7% | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 83.1% | Not measured | Only GPT-5.4 Mini measured |
| MMMU Pro (Vals AI) | 79.3% | Not measured | Only GPT-5.4 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 87.5% | Not measured | Only GPT-5.4 Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 28.1% | Not measured | Only GPT-5.4 Mini measured |
| HLE-Diamond (Artificial Analysis run) | Not measured | 25.1% | Only Muse Ember measured |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| GPQA Diamond (Epoch AI run) | 86.9% | Not measured | Only GPT-5.4 Mini measured |
| ARC-AGI | 63.7% | Not measured | Only GPT-5.4 Mini measured |
| ARC-AGI-2 | 18.9% | Not measured | Only GPT-5.4 Mini measured |
| Chess puzzles | 23.0% | Not measured | Only GPT-5.4 Mini measured |
| Mystery game puzzles | 11.0% | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: hard prompts | 1425 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: expert | 1436 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: hard prompts in English | 1420 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: homework | 1285 | Not measured | Only GPT-5.4 Mini measured |
| Kagi LLM Benchmark | 37.9% | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| AIME | 95.6% | Not measured | Only GPT-5.4 Mini measured |
| OTIS Mock AIME | 88.9% | Not measured | Only GPT-5.4 Mini measured |
| FrontierMath, tiers 1 to 3 | 51.2% | Not measured | Only GPT-5.4 Mini measured |
| FrontierMath, tier 4 | 9.8% | Not measured | Only GPT-5.4 Mini measured |
| ProofBench | 21.0% | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: math | 1418 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: mathematical work | 1428 | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 84.5% | Not measured | Only GPT-5.4 Mini measured |
| CaseLaw (Vals AI) | 51.7% | Not measured | Only GPT-5.4 Mini measured |
| CorpFin (Vals AI) | 60.9% | Not measured | Only GPT-5.4 Mini measured |
| TaxEval (Vals AI) | 71.2% | Not measured | Only GPT-5.4 Mini measured |
| MortgageTax (Vals AI) | 63.5% | Not measured | Only GPT-5.4 Mini measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| SimpleQA Verified | 29.4% | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: science | 1417 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: medicine and healthcare | 1395 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: legal and government | 1415 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: business and finance | 1418 | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| Vals Multimodal Index | 54.2% | Not measured | Only GPT-5.4 Mini measured |
| IFBench | 73.3% | Not measured | Only GPT-5.4 Mini measured |
| Long context reasoning (AA-LCR) | 77.0% | Not measured | Only GPT-5.4 Mini measured |
| GDPval-AA | 1021 | Not measured | Only GPT-5.4 Mini measured |
| DTBench | 80.0% | Not measured | Only GPT-5.4 Mini measured |
| LMCA | 40.8% | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text | 1412 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: instruction following | 1405 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: creative writing | 1369 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision | 1244 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: English | 1412 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: non-English | 1405 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Chinese | 1445 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: German | 1409 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Japanese | 1372 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Korean | 1367 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: French | 1443 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Spanish | 1405 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Russian | 1417 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: Polish | 1417 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: multi-turn | 1429 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: longer queries | 1408 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: writing and language | 1393 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Text: entertainment and media | 1372 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: English | 1244 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: Chinese | 1286 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: reading text | 1253 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: humor | 1238 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: recognizing things | 1223 | Not measured | Only GPT-5.4 Mini measured |
| LMArena Vision: creative writing | 1221 | Not measured | Only GPT-5.4 Mini measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.4 Mini | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Muse Ember costs less
Repository review
50K input + 3K output tokens
Muse Ember costs less
Agent loop
200K input + 10K output tokens
Muse Ember costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.4 Mini | Meta Muse Ember |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | March 17, 2026 | November 9, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $0.75 | $0.10 |
| Output $/Mtok | $4.50 | $0.50 |
| Coding index | 35.8 | 64.9 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $0.70 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.4 Mini better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 35.8 for GPT-5.4 Mini, though on 3 benchmarks against 5. GPT-5.4 Mini is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GPT-5.4 Mini or Muse Ember?
GPT-5.4 Mini costs $0.75 per million input tokens and $4.50 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for GPT-5.4 Mini only.
Which has the bigger context window, GPT-5.4 Mini or Muse Ember?
Muse Ember, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.4 Mini vs Muse Ember comparison based on?
8 benchmarks have a result for both models as of November 14, 2026. GPT-5.4 Mini's coding index rests on 5 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.